Face recognition, as an effective identity authentication and recognition technology, has been widely used for its convenience, user friendliness, and non-contact required, etc. But a face recognition system is also easily subjected to attacks from illegal users. Security of a face recognition system becomes a substantial concern.
The attacks against the face recognition system mainly include three types: photo attack, video attack, and 3D model attack. After obtaining the photos or videos of a valid user, outlaws or fake users may use the photos or videos of the valid user as a forged face to attempt to fraud the system. In order to distinguish real faces from photos and videos and to take precautions against possible attacks to the face recognition system, there are mainly three kinds of detection methods in conventional art: (1) distinguishing real face imaging from photo or video re-imaging through an image classification method; (2) determining by a video tracking technology that it is a real face, instead of a static photo or a 3D model; and (3) letting the user to do some actions at random by a user interaction technology, and judging whether the user's actions are correct by a computer vision technology, so as to determine that the gathered information is a live face, instead of a forged photo, a video, or a 3D model.
The method based on image classification has a low accuracy, high false determination ratio, and low security. The video tracking technology has a high accuracy of face detection, but it requires a large amount of calculations and is difficult to realize real-time processing on different terminal devices. The third method has a fast processing speed and can realize real-time processing on different terminal devices, but since its accuracy is relatively limited, it cannot defend against some deliberate attacks. Therefore, there is a need for a face in-vivo detection method and system, which can defend against deliberate attacks and improve the security of face detection systems.